The next part of MetNet-2's architecture aims at connecting each position in the layer representing the encoded context with every other position in order to capture the full context. MetNet-2 uses two-dimensional convolutional residual blocks with a sequence of exponentially increasing dilation factors of size 1, 2, 4, ..., 128 [1]}, [2]}. Dilation factors increase the receptive field of the convolution by skipping positions without increasing the number of parameters (see Figure REF ).
Each position connects in this manner to all of the other 512 \(\times \)  512 positions of the encoded tensor. Figure REF  in Supplement D illustrates the exact residual block with the dilated convolutions. Three stacks of 8 residual blocks form this context aggregating part of MetNet-2's architecture.
The target patch of precipitation that MetNet-2 predicts corresponds to 512 km \(\times \)  512 km and is centered in the middle of the 2048 km \(\times \)  2048 km of the input patch. Because of that, the 512 \(\times \)  512 positions from the context aggregation in the input encoder, are cropped to to 128 \(\times \)  128 positions. To obtain a prediction for the full size target patch, we upsample four times in each dimension, effectively creating another layer of 512 \(\times \)  512 positions. This is processed with another shallow network and ends with a categorical prediction over 512 precipitation levels for each target position. See Figure REF  for a full depiction of the architecture and Supplement  for additional architectural details.
